Sr. Machine Learning Engineer

Docusign • Seattle, WA

18h • Hybrid

About The Position

As a Senior Machine Learning Engineer, you will advance machine learning capabilities that help detect and prevent abuse and fraud across Docusign products and services. Your work will support earlier identification of harmful behavior and risk-based interventions. You will partner with Trust & Safety, Fraud, Engineering, Product, Security, and Data teams to improve detection as abuse patterns evolve.

This position is an individual contributor role reporting to the Director of Trust & Safety.

Requirements

• Bachelor’s degree or equivalent experience and 8+ years of related industry experience
• Experience developing, deploying, operating, and measuring production machine learning models, including supervised and unsupervised learning, classification, risk prediction, anomaly or behavioral detection, and model evaluation
• Experience applying ML to fraud detection, spam or abuse detection, cybersecurity, Trust & Safety, anti-abuse, risk, or another adversarial domain
• Experience solving detection problems with incomplete or evolving signals, labels, requirements, or abuse patterns, including changing attacker behavior, class imbalance, noisy labels, model degradation, and false-positive and false-negative tradeoffs
• Experience with C#, Java, or Go, and ML frameworks, data processing, feature engineering, automation, and reproducible experimentation, training, and validation
• Experience building data and feature pipelines using large-scale behavioral, event, or transactional datasets, including streaming or high-volume event processing
• Experience deploying and serving ML models in distributed systems, integrating outputs into production applications or decisioning systems, and balancing system design, scalability, reliability, latency, throughput, and model performance
• Experience with MLOps across the model lifecycle, including deployment, monitoring, retraining, versioning, degradation detection, and model health, and deploying ML workloads using Docker and Kubernetes
• Experience with observability technologies such as Prometheus, Grafana, OpenTelemetry, or Jaeger, and Azure and Azure DevOps or equivalent
• Experience defining model success criteria, measuring detection or risk improvements, communicating ML tradeoffs, and collaborating with Product, Engineering, Data, Operations, and domain experts

Nice To Haves

• Experience building ML systems for spam detection, messaging abuse, account abuse, payment fraud, account takeover, identity risk, reputation scoring, or platform integrity
• Experience developing account, entity, or behavioral risk-scoring models that combine multiple signals
• Experience detecting attackers who adapt to controls and integrating ML outputs into automated prevention, enforcement, or risk-based decisioning workflows
• Experience incorporating analyst decisions, investigations, or enforcement outcomes into model development
• Experience improving heuristic detection systems through ML or hybrid rules-and-ML approaches
• Experience establishing or scaling ML capabilities within a fraud, security, or Trust & Safety organization

Responsibilities

• Design, develop, evaluate, and deploy production ML models for fraud, spam, abuse, and other Trust & Safety risks
• Build risk and reputation scoring capabilities using behavioral, account, network, device, content, and other relevant signals
• Translate detection problems into ML requirements, including labels, features, evaluation methods, and success criteria
• Identify emerging abuse patterns and determine where ML, rules, or combined approaches improve detection
• Develop model outputs that support flagging, throttling, verification, review, or blocking
• Evaluate detection performance, false-positive and false-negative tradeoffs, and downstream risk impact
• Establish model monitoring, retraining, and degradation detection practices
• Partner with Trust & Safety and Fraud experts on adversary behavior and enforcement impact, and with Engineering, Data, Product, and Security on data and production integration
• Establish technical direction and foundational ML practices for Trust & Safety and Fraud

Benefits

• Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance.
• Stock: This role is eligible to receive Restricted Stock Units (RSUs).
• Global benefits provide options for the following: Paid Time Off: earned time off, as well as paid company holidays based on region
• Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement
• Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment
• Retirement Plans: select retirement and pension programs with potential for employer contributions
• Learning and Development: options for coaching, online courses and education reimbursements
• Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree